Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
long context
File size: 3,255 Bytes
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---
language: en
size_categories: 10K<n<100K
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
tags:
- long context
dataset_info:
- config_name: abstract
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Human Necessities
'1': Performing Operations; Transporting
'2': Chemistry; Metallurgy
'3': Textiles; Paper
'4': Fixed Constructions
'5': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
'6': Physics
'7': Electricity
'8': General tagging of new or cross-sectional technology
splits:
- name: train
num_bytes: 17225101
num_examples: 25000
- name: validation
num_bytes: 3472854
num_examples: 5000
- name: test
num_bytes: 3456733
num_examples: 5000
download_size: 12067953
dataset_size: 24154688
- config_name: patent
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Human Necessities
'1': Performing Operations; Transporting
'2': Chemistry; Metallurgy
'3': Textiles; Paper
'4': Fixed Constructions
'5': Mechanical Engineering; Lightning; Heating; Weapons; Blasting
'6': Physics
'7': Electricity
'8': General tagging of new or cross-sectional technology
splits:
- name: train
num_bytes: 466788625
num_examples: 25000
- name: validation
num_bytes: 95315107
num_examples: 5000
- name: test
num_bytes: 93844869
num_examples: 5000
download_size: 272966251
dataset_size: 655948601
configs:
- config_name: abstract
data_files:
- split: train
path: abstract/train-*
- split: validation
path: abstract/validation-*
- split: test
path: abstract/test-*
- config_name: patent
data_files:
- split: train
path: patent/train-*
- split: validation
path: patent/validation-*
- split: test
path: patent/test-*
default: true
---
**Patent Classification: a classification of Patents and abstracts (9 classes).**
This dataset is intended for long context classification (non abstract documents are longer that 512 tokens). \
Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang
* See: https://aclanthology.org/P19-1212.pdf
* See: https://evasharma.github.io/bigpatent/
It contains 9 unbalanced classes, 35k Patents and abstracts divided into 3 splits: train (25k), val (5k) and test (5k).
**Note that documents are uncased and space separated (by authors)**
Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script:
```
export MODEL_NAME=roberta-base
export MAX_SEQ_LENGTH=512
python run_glue.py \
--model_name_or_path $MODEL_NAME \
--dataset_name ccdv/patent-classification \
--do_train \
--do_eval \
--max_seq_length $MAX_SEQ_LENGTH \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 4 \
--learning_rate 2e-5 \
--num_train_epochs 1 \
--max_eval_samples 500 \
--output_dir tmp/patent
``` |